Fix all 216 pytest warnings across the test suite#995
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bruAristimunha merged 12 commits intodevelopfrom Feb 28, 2026
Merged
Fix all 216 pytest warnings across the test suite#995bruAristimunha merged 12 commits intodevelopfrom
bruAristimunha merged 12 commits intodevelopfrom
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Address root causes of warnings emitted during test runs: - Clear Epochs annotations before concatenation in paradigm processing - Replace lambda with named function in test pipelines to avoid hash warning - Suppress MNE non-standard config warning in FakeDataset (intentional usage) - Suppress OptunaSearchCV ExperimentalWarning at init via functools.wraps patch - Suppress mne_bids format conversion warnings during BIDS write - Re-apply montage after add_reference_channels for proper channel locations - Handle sklearn TimeSeriesSplit groups parameter warning in splitters - Add filterwarnings markers for pyriemann numerical issues in SSVEP tests
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- fake.py: Use os.environ instead of mne.set_config to avoid non-standard config key warnings (~60 warnings) - preprocessing.py: Remove montage before adding reference channels then re-apply, instead of suppressing location warnings - splitters.py: Use GroupsConsumerMixin to detect group-aware cv_classes and conditionally pass groups parameter (~6 warnings). LearningCurveSplitter now inherits GroupsConsumerMixin since it genuinely uses groups.
…echX/moabb into fix/resolve-test-warnings # Conflicts: # moabb/evaluations/splitters.py
…nce matrices The cross-covariance blocks passed to pyriemann's mean_covariance were not guaranteed to be well-conditioned SPD matrices, causing 'Convergence not reached' and 'invalid value encountered in log' warnings. Symmetrize each matrix and clamp small eigenvalues to bound the condition number at 1e4 before computing the Riemannian/log-Euclidean mean. Remove the corresponding filterwarnings suppressions from the test.
…ping Replace manual eigenvalue decomposition and clamping with pyriemann's Shrinkage estimator and trace normalization to regularize ill-conditioned cross-covariance matrices before computing the Riemannian mean.
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Summary
pytest moabb/tests -W all, bringing the count to 0 warningsWarning fixes by category
moabb/paradigms/base.pyconcatenate_epochsmoabb/datasets/fake.pymoabb/tests/test_evaluations.pylambda x: xwith named_identityfunctionmoabb/evaluations/utils.py__init__withfunctools.wrapsto suppress upstream warningmoabb/datasets/bids_interface.pywrite_raw_bidscall (format explicitly requested)moabb/evaluations/splitters.pysplit()call — sklearn accepts groups everywhere but warnsmoabb/tests/test_classification.py@pytest.mark.filterwarningson SSVEP_TRCA testsmoabb/datasets/preprocessing.pyadd_reference_channelsTest plan
pytest moabb/tests -W all— 1060 passed, 0 warningstest_lambda_warningstill verifies the intentional warning workstest_eval_grid_search_optunapasses (sklearn clone compatibility preserved)test_custom_inner_cvpasses with TimeSeriesSplittest_BaseImagery_cachepasses (no dtype regression)